Deep Learning for the Radiographic Detection of Apical Lesions
Published online: May 31, 2019
Abstract
Introduction
We applied deep convolutional neural networks (CNNs) to detect apical lesions (ALs) on panoramic dental radiographs.
Methods
Based
on a synthesized data set of 2001 tooth segments from panoramic
radiographs, a custom-made 7-layer deep neural network, parameterized by
a total number of 4,299,651 weights, was trained and validated via 10
times repeated group shuffling. Hyperparameters were tuned using a grid
search. Our reference test was the majority vote of 6 independent
examiners who detected ALs on an ordinal scale (0, no AL; 1, widened
periodontal ligament, uncertain AL; 2, clearly detectable lesion,
certain AL). Metrics were the area under the receiver operating
characteristic curve (AUC), sensitivity, specificity, and
positive/negative predictive values. Subgroup analysis for tooth types
was performed, and different margins of agreement of the reference test
were applied (base case: 2; sensitivity analysis: 6).
Results
The
mean (standard deviation) tooth level prevalence of both uncertain and
certain ALs was 0.16 (0.03) in the base case. The AUC of the CNN was
0.85 (0.04). Sensitivity and specificity were 0.65 (0.12) and 0.87
(0.04,) respectively. The resulting positive predictive value was 0.49
(0.10), and the negative predictive value was 0.93 (0.03). In molars,
sensitivity was significantly higher than in other tooth types, whereas
specificity was lower. When only certain ALs were assessed, the AUC was
0.89 (0.04). Increasing the margin of agreement to 6 significantly
increased the AUC to 0.95 (0.02), mainly because the sensitivity
increased to 0.74 (0.19).
Conclusions
A
moderately deep CNN trained on a limited amount of image data showed
satisfying discriminatory ability to detect ALs on panoramic
radiographs.
Comments